Before mapping, you want to know how bad the legacy data is per column. Which built-in Data Services feature does that, what must be true of the data first, and what analyses does it give?

Two S/4HANA migrations have identical record counts. One pulls from clean, recently-archived ECC data; the other consolidates several merged-company legacy systems with overlapping customers. Which one justifies Rapid Data Migration over the migration cockpit, and on what axis?

RDM can improve data quality both before and during the load rather than only after. Why does the 'during the load' window matter — what is the asymmetry if you defer all cleansing to post-go-live?

What problem does RDM's deduplication function actually prevent, and why can't you rely on the target system's primary keys to catch it instead?

A teammate says SAP Data Services profiling 'cleans the data before migration.' Correct them — what does profiling actually do, and how is that different from cleansing?

Your source landscape is a mix of non-SAP legacy systems and loose CSV files of varying quality. Which capability of SAP Data Services makes it the better fit here than a cockpit-only approach?

How are field mappings built in SAP Data Services, and why does that delivery model matter for the amount of custom code a migration carries?